1. Field
The present invention generally relates to human-machine interfaces. Specific embodiments can be used for leveraging innate and previously trained skills and abilities for high performance at operating novel devices with minimal training.
2. Related Art
Given the wide variety of tools available in modern society, learning to operate new devices is one of the most important activities in many people's lives. Unfortunately, there are many barriers to this type of learning. For example, learning to operate an unfamiliar device can require an individual to first cognitively grasp the operation of the device and then develop the requisite physical dexterity to operate the device. This is a problem both for individuals, who are forced to repeat the learning process for each new device, as well as for businesses, for whom ongoing employee (re)training is a significant and growing expense category.
One approach to dealing with modern society's profusion of devices is to focus on individualized training. There are a number of approaches which seek to make this individualized training more effective. For example, intelligent tutoring systems attempt to tailor the learning experience to the needs of individuals, thereby maximizing efficiency and effectiveness of learning. Similarly, there are a variety of systems that use virtual reality (either alone or in combination with real world interaction) in an attempt to train individuals by allowing them to mimic the operation of unfamiliar devices in simulation. For example, there exist flight simulators in which the flight controls and seat are bolted to a dynamic platform, which can provide real and simulated feedback that is appropriate and expected for a given maneuver. However, even using expensive virtual/mixed reality systems or advanced intelligent tutoring technology, this type of education-based approach still requires learners to cognitively grasp the operation of an unfamiliar device and then develop the manual dexterity to use it.
Another approach to dealing with the profusion of available devices is to map the inputs of one device onto another. An example of this is a modeling synthesizer. Modeling synthesizers in the music industry are a form of mixed reality system in which processor-enabled wind, keyboard, and string instruments serve as the primary user interface and the output is modeled on instruments that may bear no resemblance to the controlling instrument and with which the user may not be familiar. However, because the output of the modeling synthesizer does not take the form expected for the device the user is operating, the user must be skilled at interpreting the synthesized sound directly, and therefore cognizant of how their interactions with the processor-enabled instrument influence the output signal. For example, a user skilled at playing a keyboard may be able to use a keyboard-based synthesizer to create basic guitar string sounds, but lack to knowledge and skill required to use the keyboard synthesizer to create the sounds of a guitar string hammer-on and pull-off. Similar learning curve problems have been experienced with technology that is intended to allow users to compensate for injuries to their sense organs. For example, when video camera images have been translated to a grid of electrodes placed on the tongue, substantial training is required to enable blind individuals to interpret basic shapes. See Chebat, D. R., Rainville, C., Ptito, M. “Navigation Skills in the Early Blind Using a Tongue Stimulator” Soc. Neurosci. Abstr. 2007.
Among other benefits, aspects of the present disclosure can be used to enable individuals to operate unfamiliar devices while eliminating or reducing one or more of the drawbacks that characterize the prior art.